The assessment of response to stimuli is an important aspect of medical screening. Stimuli response assessment is useful for diagnosing problems with vision, feeling and hearing. Early detection of hearing difficulties is considered to be a particularly important problem. In the developed world around 20,000 infants each year are born with a significant hearing impairment. Early detection and treatment of such impairments are essential for the age-appropriate development of speech, language and cognitive ability. Therefore, most countries have initiated universal neonatal hearing screening (UNHS) programs. However, the costs of running these programs are considerable, especially in regional and remote areas. The costs associated with running a UNHS program depend on a wide variety of issues, but screening test time and referral rates are two factors that have a significant effect on program cost. Specifically, it can be shown that both halving screening times and referral rates produces more than a 20% reduction in annual costs of running a UNHS program.
There are primarily two types of devices used for neonatal hearing screening: automated auditory brainstem response (A-ABR); and transient evoked otoacoustic emission (TEOAE). A-ABR devices use earphones and surface electrodes to measure the brainstem's response to an acoustic stimulus near the hearing threshold, and then determine whether the response is an ABR waveform or random background noise.
A typical ABR waveform shows up to seven positive peaks in the first 12-15 ms post stimulus. These peaks have been shown to relate to synchronous neural activity along the auditory neural pathway from the VIIIth cranial nerve, the auditory brainstem, thalamus and thalamocortical radiations. Conventionally, ABR peaks are labelled, in latency order, using the roman numerals I to VII. Peak I typically occurs at around 2 ms with all subsequent waves following at intervals of around 1 ms. The latencies of waves I, III and V are commonly the most reliably measured with most clinicians simply identifying these peaks and then comparing their latencies to normative data matched for stimulus type, intensity and rate etc.
TEOAE devices use earphones and a microphone to measure the cochlea's response to an acoustic stimulus, and then determine whether the response is an otoacoustic emission or random background noise. Both the A-ABR and the TEOAE devices produce a pass/refer decision based on the presence or absence of a response from the brainstem (A-ABR) or the cochlea (TEOAE).
An advantage of the A-ABR is that it tests the integrity of the outer, middle and inner ear (indirectly) and the auditory nerve and brainstem (directly); whereas the TEOAE tests only the integrity of the outer and middle ear (indirectly) and the inner ear (directly). An advantage of the TEOAE is that it can be completed faster and costs less to perform. A disadvantage of the TEOAE is that it results in a higher rate of false alarms, causing significantly higher referral rates (approximately twice that of the A-ABR). These higher referral rates result in significantly increased follow-up costs and significantly higher levels of (often needless) parental anxiety.
There are two significant limitations that impede clinical use of the A-ABR as a neonatal hearing screening device:
1. The acquisition of the ABR is subject to high levels of noise interference from both external noise sources and the neonate being tested. Therefore, data acquisition times for the near-threshold ABR waveforms required for UNHS are typically around 5 minutes. Furthermore, in less favourable acquisition conditions, say with an unsettled neonate, acquisition times regularly extend to 20 minutes, after which testing is typically aborted until another time. That is not an ideal outcome as it adds to parental anxiety and can result in the neonate subsequently going home untested.
2. It is normal practice with the A-ABR to test for the presence or absence of a response at only one, near-threshold, stimulus intensity (typically 35 dB nHL) (which practice is a direct result of the lengthy ABR acquisition times.) Although a more thorough and accurate ABR test could be performed utilising multiple stimulus intensities, both above and below the hearing threshold, generally that is done only during diagnostic evaluation due to the prohibitively long test times.
The ABR waveform used for clinical interpretation is the average response waveform after the presentation of between 1000 and 4000 stimuli and is known as the ensemble average. The reason why an ensemble average is required is because of the poor signal-to-noise ratio (SNR) obtained from the presentation of any one stimulus. The poor SNR is a direct result of the evoked potential being measured in the presence of other acoustic and electrical sources, which are considered to be noise, including:                Ongoing neural activity in the brain, as measured by an electroencephalogram;        Involuntary muscular activity, such as eye and head movement;        Electromagnetic interference, such as that radiated by mains wiring and electrical equipment in the vicinity, e.g., power supplies, lights, and switches; and        Acoustic interference, such as ambient or background noise.        
Ensemble averaging is effective at reducing noise from the above four sources, with the possible exception of muscle artefacts, as the sources are all zero mean and unsynchronised to the auditory stimulus. Ensemble averaging simply assumes that the signal is deterministic and synchronised to the stimulus, whilst the noise is zero mean and not synchronised to the stimulus. Experience shows that these assumptions are generally valid.
The most commonly used stimulus in A-ABR devices is a 100 μs positive or negative going impulse, known as a broadband “click.” If the outer ear to auditory brainstem behaved as a linear system (which it generally does not) then such click stimulus would directly measure the impulse response of this system. However, it is well known that using an impulse, or in this case a periodic impulse train, is not the most efficient method to estimate the impulse response of a linear system. Other broadband stimuli such as white noise, stepped-frequencies or chirp signals enable increased signal power to be injected into the system and hence increase the SNR at the output. This response then can be directly related to the required impulse response via cross-correlation and/or Fourier analysis. One such stimulus, consisting of a pseudo-random impulse train, often referred to as a maximum length sequence (MLS), has been proposed in the prior art. The primary advantage of the MLS is that it allows for clicks to be presented before the response to the previous click has fully dissipated. This allows for an effective increase in pulse repetition frequency, also known as inter-stimulus interval (ISI) and hence results in reduced test times.
However, there are a number of issues that have impeded the wide-spread adoption of MLS stimuli:                1. The irregular ISI of the MLS leads to increased response variability, and so the ABR is not optimally reconstructed and waveforms often have (presumably contaminated) non-standard morphology;        2. Decreasing the ISI (that is, increasing the rate of stimulus presentation) results in reduced ABR amplitudes. If the increase in rate does not compensate for the decrease in ABR amplitude, then the SNR will actually worsen.        
The conventional MLS reconstruction algorithm is based on cross-correlating the response evoked by the MLS with the MLS itself. MLSs are defined so that their auto-correlation is a unit impulse and so this process effectively estimates the impulse response of the system, which ideally results in the acquired ABR. However, this reconstruction process is only optimal for responses generated by systems that are approximately linear and time-invariant. But both ABR amplitude and latency vary significantly with ISI, so conventional linear reconstruction algorithms are sub-optimal.
The application of MLS to the acquisition of the ABR was first described in 1982 by Eysholdt and Schreiner [Eysholt U. and Schreiner, C. H. R. (1982) Maximum length sequences—a fast method for measuring brain-stem-evoked potentials. Audiol, 21, 242-250]. The method of ABR reconstruction described by Eysholdt and Schreiner is based on a computationally efficient matrix inversion technique. However this method is only optimal when applied to the reconstruction of MLS signals acquired from a linear time-invariant system. Reference may also be had to U.S. Pat. No. 5,734,827 by Thornton et. al. which describes a memory efficient implementation of the conventional linear MLS reconstruction algorithm where response reconstruction is performed as the data is acquired.
Although the conventional (linear) MLS reconstruction technique has been used extensively since then, a number of alternative reconstruction techniques have been proposed that attempt to overcome the short comings of this method. For example, Van Veen and Lasky [Van Veen B. D., Lasky R. E. (1994) A Framework for Assessing the Relative Efficiency of Stimulus Sequences in Evoked Response Measurements. J Acoust Soc Am 96(4), 2235-2243] describe a framework for assessing the efficiency of MLS reconstruction sequences. They describe a method where they can select recovery sequences that maximise the signal-to-noise ratio (SNR) of the reconstructed ABR waveforms. However, in their work they limit their analysis to MLS responses that consist of a sum of scaled and shifted versions of the desired ABR impulse response, thereby ignoring the implicit variations in ABR latency.
In a more recent attempt to improve upon the conventional MLS acquisition and reconstruction techniques Jewett et al [Jewett D. L., Caplovitz G., Baird W., Trumpis M., Olson M. P. and Larson-Prior L. J. (2004) The use of QSD (Q-Sequence Deconvolution) to Recover Superposed, Transient Evoked-Responses, Clin. Neuro. 115(12), 2754-2775.] describe a q-sequence deconvolution (QSD) method that utilises stimulus sequences with minimal ISI variation (so called ‘quasi-periodic’ sequences) so as to minimise ABR latency variation. However, a major limitation of this approach is that it relies on a deconvolution operation which is conventionally implemented as a division operation in the Fourier domain. It is well known that division in frequency domain can significantly amplify noise in the signal as a result of any Fourier coefficients smaller than one. Therefore, they propose a computationally expensive, iterative procedure that attempts to find a q-sequence that meets certain pre-specified time and frequency domain constraints (including exclusion of Fourier magnitudes less than 1). It should be noted that the existence of a q-sequence that meets a given set of constraints is not assured and hence the QSD method has limited applicability.
The application of MLS to MLR (middle latency response) is described in Bell et al [Bell S. L., Allen, R and Lutman M. E. (2002) Optimizing the acquisition time of the middle latency response using maximum length sequences and chirps. J Acoust Soc Am 112(5), 2065-2073]. Bell describes varying the minimum ISI between 250 μs and 2.5 ms and measuring the associated wave (peak to trough) amplitudes and latencies. Whilst Bell reports only small (statistically insignificant) changes in observed wave latencies, there is a clear ISI-amplitude non-linearity as demonstrated through a significant decrease in wave amplitude as stimulus rate increases.
The application to MLS in TEOAE is described in U.S. Pat. No. 5,546,956 by Thornton. The preferred embodiment of the invention describes an acoustic stimulus that is measured by an aural probe inserted in the subject's ear canal. This probe consists of a microphone, with associated signal amplification, that detects the sound returned from the subject's cochlear in response to the auditory stimulus. In this work the minimum ISI is varied between 200 μs and 25 msec and the associated OAE responses show a clear decrease in amplitude as ISI is decreased. However, as the OAE is a response primarily from the mechanical, rather than neurological, portions of the middle and inner ear, there is only minimal change in latency as ISI is varied.
There is therefore a need for an improved method for acquiring a physiological response, which method overcomes many of the above described disadvantages of the prior art.